store = {}
store['args']={'name': 'emnist_multibald_bald_k10_728719', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 728719, 'acquisition_method': 'AcquisitionMethod.multibald', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 17, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=17', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.020904255319148937, 'nll': 3.864540234829517}, 'chosen_samples': [63321, 83641, 61937, 84644, 99986], 'chosen_samples_score': [0.012559334919461485, 0.02471600153821374, 0.03680620069837026, 0.048781593741674456, 0.06250840796954371], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.059202127659574465, 'nll': 44.62775650673724}, 'chosen_samples': [47139, 19594, 65641, 25050, 11981], 'chosen_samples_score': [1.0555368163638248, 1.7384238508041758, 2.0339137304943016, 2.162198505542815, 2.236117304621861], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.0777127659574468, 'nll': 33.206280215648896}, 'chosen_samples': [35839, 47554, 22093, 102485, 41674], 'chosen_samples_score': [1.4595340849611134, 2.1658692553899526, 2.2959829139432544, 2.299502956595849, 2.2962734233354682], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.10021276595744681, 'nll': 29.792044346586188}, 'chosen_samples': [80169, 109047, 76938, 80512, 108014], 'chosen_samples_score': [1.3757172075910988, 2.0753781220580167, 2.2477850136493185, 2.2929526289865922, 2.299573491203394], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.12877659574468084, 'nll': 29.71034097874418}, 'chosen_samples': [39953, 88890, 108358, 36553, 85653], 'chosen_samples_score': [1.6335119195581693, 2.157477507835485, 2.2679709423273557, 2.304735177919925, 2.2883090934614128], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.16202127659574467, 'nll': 33.059541557798994}, 'chosen_samples': [10428, 14605, 109801, 29872, 83285], 'chosen_samples_score': [1.5260802965681846, 2.1726960197163794, 2.2776597540915495, 2.298570305973077, 2.2981116946790054], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.19042553191489361, 'nll': 30.186447633783867}, 'chosen_samples': [89540, 37790, 45384, 52611, 14449], 'chosen_samples_score': [1.429421021549513, 2.06057920318467, 2.2560264953975553, 2.298983018544304, 2.313334537443017], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1950531914893617, 'nll': 28.818576676389004}, 'chosen_samples': [68726, 80222, 29505, 53651, 52670], 'chosen_samples_score': [1.57109988033306, 2.1779755421029527, 2.2762035845309554, 2.2968906977978167, 2.281379106357501], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.20553191489361702, 'nll': 24.19163953740546}, 'chosen_samples': [45603, 50004, 74591, 67965, 106030], 'chosen_samples_score': [1.6577343735337142, 2.231857661676451, 2.2908028458233565, 2.3045272067922835, 2.303766345198201], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21601063829787234, 'nll': 23.373847846173227}, 'chosen_samples': [66607, 8147, 74157, 40174, 58699], 'chosen_samples_score': [1.6241228924480011, 2.1933640385978035, 2.2894300998906463, 2.3130382753288408, 2.2897886600755535], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22994680851063828, 'nll': 22.4978631121047}, 'chosen_samples': [51105, 17621, 62148, 74264, 73878], 'chosen_samples_score': [1.6235221155182065, 2.1486649866428764, 2.2751147525843223, 2.304583648070477, 2.2921251749962073], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.24063829787234042, 'nll': 21.04259346657611}, 'chosen_samples': [93854, 39544, 19492, 64035, 77638], 'chosen_samples_score': [1.640565085663195, 2.2476526589416252, 2.295210089135054, 2.2903087258471304, 2.304500584450391], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23617021276595745, 'nll': 22.141203812944127}, 'chosen_samples': [20651, 5685, 108389, 103682, 102177], 'chosen_samples_score': [1.782107486207431, 2.236510086137763, 2.2917117677899457, 2.316551752120219, 2.314336405618932], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2517553191489362, 'nll': 20.596626172978826}, 'chosen_samples': [41850, 25278, 28672, 17264, 87117], 'chosen_samples_score': [1.6867347306747547, 2.2675814362369784, 2.2968560325103287, 2.2842262729622234, 2.2917983320932604], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2622340425531915, 'nll': 18.664076487764397}, 'chosen_samples': [32945, 106802, 44720, 100263, 45080], 'chosen_samples_score': [1.761667169673929, 2.224586443508291, 2.2883589466077012, 2.288081188491223, 2.3011171234776717], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2750531914893617, 'nll': 17.627027708013006}, 'chosen_samples': [16270, 70687, 58182, 109750, 111387], 'chosen_samples_score': [1.6828653460459078, 2.182441881966729, 2.2768583902556134, 2.2996977646821772, 2.2916241617898], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.3013297872340426, 'nll': 17.235769585142744}, 'chosen_samples': [15505, 19304, 83941, 78702, 55247], 'chosen_samples_score': [1.685526739234891, 2.1976368560312225, 2.2809259991451767, 2.298781208206741, 2.2710918393250963], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.28882978723404257, 'nll': 14.89059857794579}, 'chosen_samples': [12826, 95094, 92103, 95748, 75428], 'chosen_samples_score': [1.72784405018187, 2.250991768001881, 2.2958996271158503, 2.292800606725214, 2.295007306365288], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.30579787234042555, 'nll': 14.287001982343957}, 'chosen_samples': [90711, 38604, 88282, 58639, 73987], 'chosen_samples_score': [1.8281047343108807, 2.2485553891137653, 2.294642989255834, 2.2920826941571573, 2.290973011969805], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.30781914893617024, 'nll': 14.362867477903976}, 'chosen_samples': [77975, 100044, 35191, 25218, 29047], 'chosen_samples_score': [1.6369575221201484, 2.2382198736899896, 2.2921046912262355, 2.285200369620261, 2.297454850651353], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.30638297872340425, 'nll': 13.889079279798143}, 'chosen_samples': [23156, 90899, 30586, 37210, 49234], 'chosen_samples_score': [1.8611457881541613, 2.2735091561926635, 2.300659637683053, 2.295495815512866, 2.289366136311642], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.32457446808510637, 'nll': 13.682256113417605}, 'chosen_samples': [445, 96385, 16, 108894, 77268], 'chosen_samples_score': [1.6852806350722076, 2.2292589421042726, 2.293758913111681, 2.330826631695029, 2.30731175153474], 'chosen_samples_orignal_score': None})
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